机器人匹配：使用递归最近邻搜索的社会机器人检测
Bot-Match: Social Bot Detection with Recursive Nearest Neighbors Search
摘要：
社交机器人在过去十年中出现，最初造成了滋扰，而最近被用来恐吓记者，影响选举活动，并加剧现有的社会分歧。这种社会威胁催生了一场机器人检测算法竞赛，其中检测算法不断发展，试图跟上日益复杂的机器人账户。这种猫和老鼠的循环揭示了监督式机器学习算法的局限性，研究人员试图利用昨天的数据来预测明天的机器人。这一差距意味着研究人员、记者和分析师每天都要识别出最先进的监督机器人检测算法无法检测到的恶意机器人账户。这些分析师通常希望在不标记/训练新模型的情况下找到相似的机器人帐户，其中相似性可以由内容，网络位置或两者来定义。基于相似性的算法可以补充现有的监督和无监督方法，并填补这一空白。为此，我们提出了机器人匹配方法，在该方法中，我们评估了社交媒体嵌入，使半监督递归最近邻搜索能够映射一个或多个种子帐户的新兴社会网络安全威胁。
英文摘要：
Social bots have emerged over the last decade, initially creating a nuisance while more recently used to intimidate journalists, sway electoral events, and aggravate existing social fissures. This social threat has spawned a bot detection algorithms race in which detection algorithms evolve in an attempt to keep up with increasingly sophisticated bot accounts. This cat and mouse cycle has illuminated the limitations of supervised machine learning algorithms, where researchers attempt to use yesterday's data to predict tomorrow's bots. This gap means that researchers, journalists, and analysts daily identify malicious bot accounts that are undetected by state of the art supervised bot detection algorithms. These analysts often desire to find similar bot accounts without labeling/training a new model, where similarity can be defined by content, network position, or both. A similarity based algorithm could complement existing supervised and unsupervised methods and fill this gap. To this end, we present the Bot-Match methodology in which we evaluate social media embeddings that enable a semi-supervised recursive nearest neighbors search to map an emerging social cybersecurity threat given one or more seed accounts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
